更新时间:2023-02-26 20:12:17
如果您还没有这样做,请查看 CRAN 上的时间序列视图,尤其是关于多元时间序列的部分.
If you haven't done so already, have a look at the time series view on CRAN, especially the section on multivariate time series.
在金融领域,一种传统的方法是使用因子模型,通常使用 BARRA 或 Fama-French 类型模型.Eric Zivot 的 使用 S-PLUS 建模金融时间序列" 很好地概述了这些主题,但它不能立即转移到 R. Ruey Tsay 的分析of Financial Time Series"(可在 CRAN 上的 TSA 包中找到)在第 9 章中也对因子模型和主成分分析进行了很好的讨论.
In finance, one traditional way of doing this is with a factor model, frequently with either a BARRA or Fama-French type model. Eric Zivot's "Modeling financial time series with S-PLUS" gives a good overview of these topics, but it isn't immediately transferable into R. Ruey Tsay's "Analysis of Financial Time Series" (available in the TSA package on CRAN) also has a nice discussion of factor models and principal component analysis in chapter 9.
R 还提供了许多涵盖 矢量自回归 (VAR) 模型的软件包.特别是,我建议查看 Bernhard Pfaff 的 VAR Modeling (vars) 包和相关小插图.
R also has a number of packages that cover vector autoregression (VAR) models. In particular, I would recommend looking at Bernhard Pfaff's VAR Modelling (vars) package and the related vignette.
我强烈建议您查看 Ruey Tsay 的主页因为它涵盖了所有这些主题,并提供了必要的 R 代码.特别是,请查看应用多元分析","金融时间序列分析"和多元时间序列分析" 课程.
I strongly recommend looking at Ruey Tsay's homepage because it covers all these topics, and provides the necessary R code. In particular, look at the "Applied Multivariate Analysis", "Analysis of Financial Time Series", and "Multivariate Time Series Analysis" courses.
这是一个非常大的主题,有很多好书涵盖了它,包括多元时间序列预测和季节性.还有一些:
This is a very large subject and there are many good books that cover it, including both multivariate time series forcasting and seasonality. Here are a few more: